Urodynamic signals represent the integrated function of detrusor contractility, outlet resistance, and neural control mechanisms. Hobbs et al. [14] utilized 805 urodynamic studies from 546 patients with spina bifida to train a support vector machine classifier for detecting detrusor overactivity, achieving an area under the receiver operating characteristic curve (AUC) of 0.919. This study demonstrated that AI could replicate, and in some cases surpass, manual expert interpretation in pediatric neurogenic bladder cohorts. Similarly, Choo et al. [15] developed an automatic interpretation algorithm for uroflowmetry using a convolutional neural network architecture, which achieved 90% accuracy in classifying normal, obstructed, and interrupted flow patterns (Table 1). Together, these studies highlight how standard clinical signals (pressure and flow curves) can serve as robust, high-dimensional input domains for AI-driven diagnostic automation.

Recent advances in DL have further extended AI’s role from static classification to real-time recognition of dynamic bladder events. Liu et al. [16] implemented a YOLOv5-based real-time recognition system trained on urodynamic traces from neurogenic bladder patients, achieving >95% accuracy in identifying detrusor contraction events and voiding phases. This model represented one of the first practical demonstrations of real-time urodynamic interpretation using DL architectures capable of temporal and spatial feature fusion. Similarly, Cho and Youn [17] developed an intravesical pressure-mapping algorithm utilizing a hybrid DL framework that interprets bladder function and predicts abnormal voiding patterns with an AUC of 0.93 (Table 1). Their approach introduced the novel concept of AI-assisted bladder functional mapping, bridging traditional pressure–volume analysis with data-driven interpretability. As illustrated in Fig. 2, these algorithms process urodynamic signals through a sequential AI pipeline involving data acquisition, preprocessing, feature extraction, model training, and clinical output generation.

AI-based automated interpretation systems provide substantial clinical benefits by reducing operator dependency, standardizing evaluation criteria, and improving reproducibility across institutions. They also facilitate the extraction of quantitative urodynamic biomarkers —including pressure variability, compliance slope, contraction amplitude, and voiding time—that can subsequently inform predictive models of treatment response and failure. Ultimately, integrating these automated systems into electronic health record platforms could enable continuous, data-driven urodynamic monitoring, representing a key step toward fully digitalized and personalized neurourology. Collectively, these developments in automated urodynamic interpretation establish the technical foundation upon which AI-based predictive models for treatment failure can be built.

Recent studies have extended automated urodynamic analysis into predictive modeling for treatment response and failure, applying multimodal and transformer-based DL frameworks [18, 19].

These approaches demonstrate progressive integration of urodynamic, EMG, and clinical features, marking a shift from diagnostic automation to prognostic precision. By capturing temporal patterns, multimodal relationships, and neural correlates, such models exemplify how AI is transforming urodynamic interpretation into a foundation for personalized therapy.

Collectively, these advances not only facilitate diagnostic standardization but also generate structured urodynamic biomarkers that serve as inputs for predictive modeling. As summarized in Table 2, recent frameworks have begun integrating urodynamic, EMG, and clinical variables to forecast therapeutic nonresponse, relapse, or device failure, effectively extending AI’s role from diagnostic automation to prognostic precision.

Başaranoğlu et al. analyzed 847 patients with overactive bladder (OAB) treated with anticholinergics or beta-3 agonists using a gradient boosting classifier, achieving an AUC of 0.91 (95% confidence interval [CI], 0.87–0.95) and 87% accuracy, significantly outperforming clinician predictions (AUC, 0.71; P < 0.001) [20]. The model incorporated 23 clinical and urodynamic features, identifying baseline voiding frequency, nocturia, maximum cystometric capacity, and detrusor overactivity as top predictors. However, as the model was validated only through 10-fold internal cross-validation within a single tertiary center, its external generalizability remains uncertain [28, 29].

Wang et al. [33] developed a least absolute shrinkage and selection operator (LASSO) regression–based nomogram predicting upper urinary tract damage in 301 neurogenic bladder patients, achieving a concordance index (C-index) of 0.80 (95% CI, 0.75–0.84) and 78% accuracy. From 31 candidate variables, 12 were retained as key predictors, including detrusor leak point pressure, bladder compliance, vesicoureteral reflux, and bladder management type. The nomogram is clinically interpretable and easily applicable at the bedside but has not yet undergone external validation.

Lai et al. implemented a graph neural network model (GNN) in 1,064 patients from the Lower Urinary Tract Dysfunction Research Network cohort, achieving an AUC of 0.76 (95% CI, 0.72–0.80) and 71% accuracy in classifying pharmacotherapy responders [22, 27]. The GNN architecture encoded relationships among lower urinary tract symptom variables, outperforming logistic regression (AUC, 0.72) but requiring advanced computational infrastructure and expertise. While promising, the modest performance improvement raises questions about its cost-effectiveness for clinical deployment.

Karmonik et al. [23] employed random forest modeling on functional magnetic resonance imaging connectivity maps from 27 women with MS, achieving 86% accuracy in classifying voiding dysfunction. Connectivity between the pontine micturition center and the prefrontal cortex emerged as the most discriminative feature, suggesting that neuroimaging biomarkers can enhance prediction of neurogenic voiding dysfunction. However, the small sample size raises concerns about overfitting and limited external validation [28].

Most published ML studies in neurourology rely on internal validation (cross-validation or bootstrapping) rather than independent dataset testing [28, 29]. The 4 core studies reviewed here were all single-center and retrospective, with sample sizes ranging from 27 to 1,064, underscoring the high risk of overfitting and limited demographic diversity. Recent multicenter investigations have begun to address these limitations. As shown in Fig. 3, the relationship between dataset size and model performance indicates that smaller cohorts (e.g., Karmonik et al. [23]) tend to exhibit inflated AUC values, whereas larger datasets (e.g., Başaranoğlu et al. [20]) yield more generalizable results. Quantitatively, smaller cohorts (<100 cases) often report AUC values around 0.86–0.90 despite limited external validation, while larger multicenter datasets ( >500 cases) achieve slightly lower but more reproducible performance (mean AUC ≈ 0.83 ±0.06). Lee et al. [30] externally validated a dual-center ML model predicting multidrug-resistant urinary tract infections in SCI patients (AUC, 0.83), and Werneburg et al. [31] demonstrated external reproducibility of an OAB outcome prediction model (AUC, 0.89). Increasingly, adherence to model reporting standards such as TRIPOD-ML and PROBAST-AI is emphasized to ensure reproducibility and real-world reliability [32, 34-38].

ML models in neurourology integrate diverse inputs, including demographics, neurological diagnosis, comorbidities, symptom scores, urodynamic parameters, and neuroimaging features [21-24]. As shown in Fig. 4, key determinants of treatment response consistently include urgency frequency, bladder compliance, detrusor leak pressure, and cortical–pontine connectivity, reflecting multilayer mechanisms of lower urinary tract dysfunction that traditional analyses fail to capture. These representative models —gradient boosting, LASSO regression, graph neural network, and random forest —demonstrate how distinct modalities converge on overlapping predictors that drive both accuracy and interpretability [39].

While ensemble models such as gradient boosting and random forest often achieve superior predictive accuracy, their “black box” nature limits transparency and clinician confidence [40]. Interpretable approaches —including LASSO regression and attention-weighted GNNs —offer more intuitive reasoning, while post hoc explainability tools such as SHAP feature maps (Fig. 5) enhance model transparency and clinical trust [21, 22, 41, 42]. Successful clinical adoption requires embedding these models into user-friendly decision-support interfaces codesigned with clinicians and refined through iterative feedback [34, 38, 43]. Ultimately, predictive modeling in neurourology exemplifies AI’s potential to transform empirical treatment decisions into transparent, patient-specific strategies, provided that interpretability, validation, and ethical oversight remain central [37, 38].